Data-driven Cyberattack Detection for Photovoltaic (PV) Systems through Analyzing Micro-PMU Data

Li, Qi; Li, Fangyu; Zhang, Jinan; Ye, Jin; Song, Wenzhan; Mantooth, Alan

doi:10.1109/ecce44975.2020.9236274

Data-driven Cyberattack Detection for Photovoltaic (PV) Systems through Analyzing Micro-PMU Data

Journal Article · Sun Oct 11 00:00:00 EDT 2020 · IEEE Energy Conversion Congress and Exposition (ECCE)

DOI:https://doi.org/10.1109/ecce44975.2020.9236274· OSTI ID:2341852

Li, Qi ^[1]; Li, Fangyu ^[2]; Zhang, Jinan ^[2]; Ye, Jin ^[2]; Song, Wenzhan ^[2]; Mantooth, Alan ^[3]

University of Georgia, Athens, GA (United States); University of Arkansas
University of Georgia, Athens, GA (United States)
University of Arkansas, Fayetteville, AR (United States)

With increasing exposure to software-based sensing and control, Photovoltaic (PV) systems are facing higher risks of cyber attacks. Here, to ensure the system stability and minimize potential economic losses, it is imperative to monitor operating states and detect attacks at the early stage. To meet this demand, Micro-Phasor Measurement Units (μPMU) are increasingly popular in monitoring distribution networks. However, due to the relatively low sampling rate, μPMU has not yet been used to detect and classify cyber-attacks in power electronics enabled smart grid. To our knowledge, this is one of the first attempts to use μPMU to detect cyber attacks that degrade the performance of power electronics systems. We propose to apply data-driven methods on micro-PMU data to implement attack detection. We have evaluated data-driven methods, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network (CNN). The proposed CNN model achieves the required performances with the highest 99.23% accuracy and 0.9963 F ₁ score.

View Accepted Manuscript (DOE)

Research Organization:: University of Arkansas, Fayetteville, AR (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0009026

OSTI ID:: 2341852

Journal Information:: IEEE Energy Conversion Congress and Exposition (ECCE), Journal Name: IEEE Energy Conversion Congress and Exposition (ECCE) Vol. 2020; ISSN 2329-3721

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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